Targeted computational analysis of the C3HEB/FEJ mouse model for drug efficacy testing

2020 Spring.Includes bibliographical references.Efforts to develop effective and safe drugs for the treatment of tuberculosis (TB) require preclinical evaluation in animal models. Alongside efficacy testing of novel therapies, effects on pulmonary pathology and disease progression are monitored by using histopathology images from these infected animals. To compare the severity of disease across treatment cohorts, pathologists have historically assigned a semi-quantitative histopathology score that may be subjective in terms of their training, experience, and personal bias. Manual histopathology, therefore, has limitations regarding reproducibility between studies and pathologists, potentially masking successful treatments. This report describes a pathologist-assistive software tool that reduces these user limitations while providing a rapid, quantitative scoring system for digital histopathology image analysis. The software, called 'Lesion Image Recognition and Analysis' (LIRA), employs convolutional neural networks to classify seven different pathology features, including three different lesion types from pulmonary tissues of the C3HeB/FeJ tuberculosis mouse model. LIRA was developed to improve the efficiency of histopathology analysis for mouse tuberculosis infection models. The model approach also has broader applications to other diseases and tissues. This also includes animals that are undergoing anti-mycobacterial treatment and host immune system modulation. A complimentary software package called 'Mycobacterial Image Analysis' (MIA) had also been developed that characterizes the varying bacilli characteristics such as density, aggregate/planktonic bacilli size, fluorescent intensity, and total counts. This further groups the bacilli characteristic data depending on the seven different classifications that are selected by the user. Using this approach allows for an even more targeted analysis approach that can determine how therapy and microenvironments influence the Mtb response

Glioma on Chips Analysis of glioma cell guidance and interaction in microfluidic-controlled microenvironment enabled by machine learning

In biosystems, chemical and physical fields established by gradients guide cell migration, which is a fundamental phenomenon underlying physiological and pathophysiological processes such as development, morphogenesis, wound healing, and cancer metastasis. Cells in the supportive tissue of the brain, glia, are electrically stimulated by the local field potentials from neuronal activities. How the electric field may influence glial cells is yet fully understood. Furthermore, the cancer of glia, glioma, is not only the most common type of brain cancer, but the high-grade form of it (glioblastoma) is particularly aggressive with cells migrating into the surrounding tissues (infiltration) and contribute to poor prognosis. In this thesis, I investigate how electric fields in the microenvironment can affect the migration of glioblastoma cells using a versatile microsystem I have developed. I employ a hybrid microfluidic design to combine poly(methylmethacrylate) (PMMA) and poly(dimethylsiloxane) (PDMS), two of the most common materials for microfluidic fabrication. The advantages of the two materials can be complemented while disadvantages can be mitigated. The hybrid microfluidics have advantages such as versatile 3D layouts in PMMA, high dimensional accuracy in PDMS, and rapid prototype turnaround by facile bonding between PMMA and PDMS using a dual-energy double sided tape. To accurately analyze label-free cell migration, a machine learning software, Usiigaci, is developed to automatically segment, track, and analyze single cell movement and morphological changes under phase contrast microscopy. The hybrid microfluidic chip is then used to study the migration of glioblastoma cell models, T98G and U-251MG, in electric field (electrotaxis). The influence of extracellular matrix and chemical ligands on glioblastoma electrotaxis are investigated. I further test if voltage-gated calcium channels are involved in glioblastoma electrotaxis. The electrotaxes of glioblastoma cells are found to require optimal laminin extracellular matrices and depend on different types of voltage-gated calcium channels, voltage-gated potassium channels, and sodium transporters. A reversiblysealed hybrid microfluidic chip is developed to study how electric field and laminar shear can condition confluent endothelial cells and if the biomimetic conditions affect glioma cell adhesion to them. It is found that glioma/endothelial adhesion is mediated by the Ang1/Tie2 signaling axis and adhesion of glioma is slightly increased to endothelial cells conditioned with shear flow and moderate electric field. In conclusion, robust and versatile hybrid microsystems are employed for studying glioma biology with emphasis on cell migration. The hybrid microfluidic tools can enable us to elucidate fundamental mechanisms in the field of the tumor biology and regenerative medicine.Okinawa Institute of Science and Technology Graduate Universit

On Computable Protein Functions

Proteins are biological machines that perform the majority of functions necessary for life. Nature has evolved many different proteins, each of which perform a subset of an organism’s functional repertoire. One aim of biology is to solve the sparse high dimensional problem of annotating all proteins with their true functions. Experimental characterisation remains the gold standard for assigning function, but is a major bottleneck due to resource scarcity. In this thesis, we develop a variety of computational methods to predict protein function, reduce the functional search space for proteins, and guide the design of experimental studies. Our methods take two distinct approaches: protein-centric methods that predict the functions of a given protein, and function-centric methods that predict which proteins perform a given function. We applied our methods to help solve a number of open problems in biology. First, we identified new proteins involved in the progression of Alzheimer’s disease using proteomics data of brains from a fly model of the disease. Second, we predicted novel plastic hydrolase enzymes in a large data set of 1.1 billion protein sequences from metagenomes. Finally, we optimised a neural network method that extracts a small number of informative features from protein networks, which we used to predict functions of fission yeast proteins

Micro/nanofluidic and lab-on-a-chip devices for biomedical applications

Micro/Nanofluidic and lab-on-a-chip devices have been increasingly used in biomedical research [1]. Because of their adaptability, feasibility, and cost-efficiency, these devices can revolutionize the future of preclinical technologies. Furthermore, they allow insights into the performance and toxic effects of responsive drug delivery nanocarriers to be obtained, which consequently allow the shortcomings of two/three-dimensional static cultures and animal testing to be overcome and help to reduce drug development costs and time [2–4]. With the constant advancements in biomedical technology, the development of enhanced microfluidic devices has accelerated, and numerous models have been reported. Given the multidisciplinary of this Special Issue (SI), papers on different subjects were published making a total of 14 contributions, 10 original research papers, and 4 review papers. The review paper of Ko et al. [1] provides a comprehensive overview of the significant advancements in engineered organ-on-a-chip research in a general way while in the review presented by Kanabekova and colleagues [2], a thorough analysis of microphysiological platforms used for modeling liver diseases can be found. To get a summary of the numerical models of microfluidic organ-on-a-chip devices developed in recent years, the review presented by Carvalho et al. [5] can be read. On the other hand, Maia et al. [6] report a systematic review of the diagnosis methods developed for COVID-19, providing an overview of the advancements made since the start of the pandemic. In the following, a brief summary of the research papers published in this SI will be presented, with organs-on-a-chip, microfluidic devices for detection, and device optimization having been identified as the main topics.info:eu-repo/semantics/publishedVersio

Using Whole Genome Sequencing to Track Colibacillosis on Saskatchewan Broiler Flocks

Colibacillosis is a systemic infection caused by Escherichia coli resulting in significant morbidity and mortality in broiler flocks worldwide. Little is known about the group of E. coli that cause colibacillosis, collectively termed avian pathogenic E. coli (APEC). My MSc research focused on determining how APEC differ from resident E. coli that live in the chicken gut but do not cause disease. I hypothesized that systemic and cecal E. coli are genetically distinct, and E. coli that cause colibacillosis are virulent outbreak strains. My objectives were to isolate E. coli from Saskatchewan broilers, sequence their genomes using Nanopore and Illumina technology, and screen them for virulence, antimicrobial resistance, and disinfectant resistance. I developed a pipeline to isolate and sequence E. coli from Saskatchewan colibacillosis outbreaks, selecting isolates based on outbreak, disease status, and biofilm profiles. I sequenced 96 E. coli isolates, consisting of 58 from diseased broilers with confirmed colibacillosis (systemic E. coli), and 38 from the cecal contents of healthy broilers in the same flocks (cecal E. coli). Our initial experiments were optimized for whole genome assembly and excluded DNA fragments under 500bp; therefore, we likely missed plasmids present in E. coli isolates. I tested six plasmid kits and two sequencing protocols to develop a methodology to capture missed plasmids in avian E. coli isolates and successfully identified new plasmids in both types of isolates. Systemic E. coli were more drug-resistant than cecal E. coli against a panel of 27 antimicrobial agents and possessed significantly more plasmids than cecal E. coli. plasmids contained multiple virulence and antimicrobial resistance genes that may contribute to disease. Since biofilms can provide protection from antibiotics and disinfectants, I quantified biofilm formation in three different medias. Systemic isolates were significantly more likely to form biofilms in rich media, but there was no correlation between biofilm formation and antimicrobial resistance. My characterization led us to conclude that systemic and cecal E. coli represent two different populations of strains. This will need to be confirmed with the analysis of more isolates. Characterization of avian pathogenic E. coli will help us understand how these isolates cause disease

2020 IMSAloquium

Welcome to IMSAloquium 2020. This is IMSA’s 33rd year of leading in educational innovation, and the 32nd year of the IMSA Student Inquiry and Research (SIR) Program.https://digitalcommons.imsa.edu/archives_sir/1030/thumbnail.jp

Accessible software frameworks for reproducible image analysis of host-pathogen interactions

Um die Mechanismen hinter lebensgefährlichen Krankheiten zu verstehen, müssen die zugrundeliegenden Interaktionen zwischen den Wirtszellen und krankheitserregenden Mikroorganismen bekannt sein. Die kontinuierlichen Verbesserungen in bildgebenden Verfahren und Computertechnologien ermöglichen die Anwendung von Methoden aus der bildbasierten Systembiologie, welche moderne Computeralgorithmen benutzt um das Verhalten von Zellen, Geweben oder ganzen Organen präzise zu messen. Um den Standards des digitalen Managements von Forschungsdaten zu genügen, müssen Algorithmen den FAIR-Prinzipien (Findability, Accessibility, Interoperability, and Reusability) entsprechen und zur Verbreitung ebenjener in der wissenschaftlichen Gemeinschaft beitragen. Dies ist insbesondere wichtig für interdisziplinäre Teams bestehend aus Experimentatoren und Informatikern, in denen Computerprogramme zur Verbesserung der Kommunikation und schnellerer Adaption von neuen Technologien beitragen können. In dieser Arbeit wurden daher Software-Frameworks entwickelt, welche dazu beitragen die FAIR-Prinzipien durch die Entwicklung von standardisierten, reproduzierbaren, hochperformanten, und leicht zugänglichen Softwarepaketen zur Quantifizierung von Interaktionen in biologischen System zu verbreiten. Zusammenfassend zeigt diese Arbeit wie Software-Frameworks zu der Charakterisierung von Interaktionen zwischen Wirtszellen und Pathogenen beitragen können, indem der Entwurf und die Anwendung von quantitativen und FAIR-kompatiblen Bildanalyseprogrammen vereinfacht werden. Diese Verbesserungen erleichtern zukünftige Kollaborationen mit Lebenswissenschaftlern und Medizinern, was nach dem Prinzip der bildbasierten Systembiologie zur Entwicklung von neuen Experimenten, Bildgebungsverfahren, Algorithmen, und Computermodellen führen wird

A system biology study of the molecular mechanism of Metformin on breast cancer

Breast cancer is a complicated, heterogeneous and diversified disease that comprises of a mixture of various subtypes. The emergence of high throughout technologies such as gene expression profiling and DNA copy number analysis has allowed a profound awareness of this complex disease. Breast cancer molecular classification began with ER, PR and HER2 based stratification of patients, and further classified into various intrinsic subtypes such as Luminal A, Luminal B, Her-2 enriched, Basal-like, and Claudin-low. These subtypes were proven to have significant divergences in amplified and mutated genes, survival rates, prognosis and response to therapy. The anti-diabetic drug Metformin has been prescribed to treat type 2 diabetes patients since 1957 with a well-established side effects and safety profile. Additionally, cancer researchers have studied the anti-tumour effects of this drug since 2005 and have determined Metformin benefit in the reduction of cancers incidence. Thus, Metformin has been suggested as an ideal candidate to treat and prevent diverse types of cancers, including breast carcinoma. The purpose of this study is to elucidate the biological and molecular effects of Metformin on breast cancer cell lines such as BT-474, MCF-7, MDA-MB-231, MDA-MB-468, and SkBr3. Interestingly, Metformin treatments reduced the viability and proliferation of examined breast cancer cell lines, while induced cells apoptosis. Basal-like (MDA-MB-468) was the most sensitive phenotype to Metformin treatments, whereas HER2 (SkBr3) was the least sensitive subtype. We performed Gene Expression Microarray and NanoString analysis of Metformin treated MDAMB- 468 and SkBr3 cells and found that the upregulated Protein Tyrosine Kinase 2 Beta (PTK2B) was predominantly involved in cell proliferation, cell survival, cell migration and cell invasion. We found that PTK2B promotes invasion and migration, while prevents the proliferation of breast cancer cells. It is also, playing a vital role in Autocrine Somatotropin signalling pathway in breast cancer. Additionally, Mass Spectrometry (MS) analyses and pathways enrichment analysis that has emphasised the role of PTK2B in breast cancer invasion and metastasis. Additionally, the combined action of both selected markers and Metformin treatment on fundamental biological functions in breast cancer were also assessed. These data showed that Metformin promotes Her-2 enriched breast cancer invasion through mechanisms involving PYK2, and that future treatments should consider potential complications resulting from metformin-based therapies

Innovation Meets Tradition in the Sheep and Goat Dairy Industry

The domestic sheep (Ovis aries) and goat (Capra aegagrus hircus) are small ruminant species widely distributed throughout the world. They were among the first animals to be domesticated. Owing to their small stature and versatility, sheep and goats still are one of the most important food source in many arid regions. Traditionally, autochthonous breeds with a strong milk production seasonality were reared in extensive production systems, on a smallholder farming basis. The huge number and variety of their dairy products reflect the different cultures and traditions of vast areas of the world. However, today the traditional ovine and caprine dairy production chain, from farmers to exporters, is facing the challenges of innovation, sustainability, safety, and productivity, while at the same time protecting each product’s individual characteristics. This Special Issue is dedicated to the field of ovine and caprine dairy production with ground-breaking perspectives and approaches, from physical-chemistry studies on milk and dairy, to new feeding strategies, herd management, nutritional quality, animal welfare, sustainability, and omics studies